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Longitudinal analysis of sentiment and emotion in news media headlines using automated labelling with Transformer language models
This work describes a chronological (2000–2019) analysis of sentiment and emotion in 23 million headlines from 47 news media outlets popular in the United States. We use Transformer language models fine-tuned for detection of sentiment (positive, negative) and Ekman’s six basic emotions (anger, disg...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9578611/ https://www.ncbi.nlm.nih.gov/pubmed/36256658 http://dx.doi.org/10.1371/journal.pone.0276367 |